QDRL: QoS-Aware Deep Reinforcement Learning Approach for Tor's Circuit Scheduling-Reference-Cited by-同舟云学术

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QDRL: QoS-Aware Deep Reinforcement Learning Approach for Tor's Circuit Scheduling

Published:2022-09-01 Issue:5 Volume:9 Page:3396-3410
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Basyoni Lamiaa¹^ORCID,Erbad Aiman²^ORCID,Mohamed Amr³^ORCID,Guizani Mohsen³^ORCID

Affiliation:

1. Kindi Center for Computing Research, Qatar University, Doha, Qatar

2. Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

3. Computer Science and Engineering Department, College of Engineering, Qatar University, Doha, Qatar

Funder

Qatar National Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/6488902/9882997/09786641.pdf?arnumber=9786641

Reference54 articles.

1. User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach

2. Fair Resource Allocation Based on Deep Reinforcement Learning in Fog Networks

3. mTor: A multipath Tor routing beyond bandwidth throttling;yang;Proc IEEE Conf Commun Netw Secur,0

4. Tor is unfair–and what to do about it;tschorsch;Proc IEEE 36th Conf Local Comput Netw,0

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